A survey of graph-based algorithms for discovering business processes

Sarno, Riyanarto; Sungkono, Kelly Rossa

doi:10.26555/ijain.v5i2.296

Cited by 19 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to comparing the quality of obtained results, the time complexity of the algorithms is measured. The time complexity of α $ is O(n 4 ) [33], while the time complexity of GIT is O(n 3 ), with the explanation steps as listed in Table 9.…”

Section: Resultsmentioning

confidence: 99%

Improving efficiency for discovering business processes containing invisible tasks in non-free choice

et al. 2021

Self Cite

View full text Add to dashboard Cite

Process discovery helps companies automatically discover their existing business processes based on the vast, stored event log. The process discovery algorithms have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by $$\alpha ^{\$ }$$ α $ method, is a type of relationship that combines the non-free choice and the invisible task. $$\alpha ^{\$ }$$ α $ proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of $$\alpha ^{\$ }$$ α $ check the combination of invisible task within non-free choice. The checking processes are time-consuming and result in high computing times of $$\alpha ^{\$ }$$ α $ . This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relationships of the graphs. The analysis of the graph relationships by rules of GIT is more efficient than the iterative process of checking combined activities by $$\alpha ^{\$ }$$ α $ . This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Graph database gains highest storing computing time of batch event logs; however, this database obtains low storing computing time of streaming event logs. Furthermore, based on an event log with 99 traces, GIT algorithm discovers a process model 42 times faster than α++ and 43 times faster than α$. GIT algorithm can also handle 981 traces, while α++ and α$ has maximum traces at 99 traces. Discovering a process model by GIT algorithm has less time complexity than that by $$\alpha ^{\$ }$$ α $ , wherein GIT obtains $$O(n^{3} )$$ O ( n 3 ) and $$\alpha ^{\$ }$$ α $ obtains $$O(n^{4} )$$ O ( n 4 ) . Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

show abstract

Section: Resultsmentioning

confidence: 99%

Improving efficiency for discovering business processes containing invisible tasks in non-free choice

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to comparing the results of fitness and precision, the time complexity of the algorithms is measured. The time complexity of $ is ( 4 ) [31], while the time complexity of GIT is 3, with the explanation steps as listed in Table 9.…”

Section: Resultsmentioning

confidence: 99%

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

Sarno

Sungkono

Taufiqulsa’di

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Process discovery helps companies to automatically discover their existing business processes based on the huge, stored event log. The algorithms of process discovery have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by α $ method, is a type of relation that combines the non-free choice and the invisible task. α $ proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of α $ check the combination of invisible task within non-free choice. The checking processes is time consuming, and results in high computing times of α $. This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relations of the graphs. The analysis of the graph relations by rules of GIT is more efficient than the iterative process of checking combined activities by α $. This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Storing a streaming event log in a graph-database has the lowest computing time than storing in other databases, i.e., SQL and MongoDB. Discovering a process model by GIT algorithm has less time complexity than that by α $, wherein GIT obtains O(n3) and α $ obtains O(n4) . In terms of computing time, GIT algorithm is 0.89 faster on batch event log and 0.85 seconds faster on streaming event log than α $. Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

show abstract

“…A graph-based process model is a process model that is obtained through a graph-based algorithm applied to a graph database. The advantage of implementing a graph-based algorithm in business process modeling is because the graph database stores not only activities but also relationships so that the algorithm designed can produce a low time complexity [10].…”

Section: Graph-based Process Modelmentioning

confidence: 99%

“…Several methods are known for discovering business process models from event logs, including alpha miner [2], heuristic miner [3], inductive miner [4], fuzzy miner [5], split miner [6,7], and graphbased miner [8,9]. Graph-based miner algorithms outperform others in lower time complexity [10]. This algorithm is applied to Neo4j graph database, which stores activities and relationships.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Waspada¹,

Sarno²,

Sungkono³

2020

IJIES

Self Cite

View full text Add to dashboard Cite

The existing method of graph-based process model discovery has weaknesses in detecting parallel relationship (XOR, AND, and OR). The algorithm only works on a particular graph structure, so it must be reconfigured when applied to other different structures. To answer this problem, this paper proposes an improved method of parallel model detection, which is designed in two phases. The first one consists of three steps; firstly is to count and record the value of relationship frequency into every node in a graph model. Then, the second step implements the algorithm to discover the concurrent relationship. The third step detects all possible split and join relationships. Based on the first phase, then a consistent and robust parallel discovery algorithm can be developed. The first parallel algorithm is to identify the XOR relationship. This algorithm is designed with the rule that the XOR pattern cannot have a concurrent relationship between its branch nodes. Next, the algorithm for detecting AND and OR must detect the existence of any concurrent relationship in its branches. Then, AND and OR pattern is differentiated by their unique characteristic of relationship frequency at branch nodes. To verify the ability of the proposed methods in which the existing method fails, we have designed four scenarios. Scenario 1 and 2 consecutively were arranged with two and three branches parallel model. Scenario 3 located the AND and OR inside the XOR pattern. In scenario 4 the sequence relationships were inserted between split and join of parallel patterns. The experimental results show that the proposed method successfully recognizes and differentiates XOR, AND and OR patterns correctly in all scenarios. It also sounds in all discovered model and get 100% fitness.

show abstract

A survey of graph-based algorithms for discovering business processes

Cited by 19 publications

References 26 publications

Improving efficiency for discovering business processes containing invisible tasks in non-free choice

Improving efficiency for discovering business processes containing invisible tasks in non-free choice

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Contact Info

Product

Resources

About